Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 14 min ago
Peer-reviewedCC BY-NC-ND 4.0Nature Communications

Systematic comparison of affinity plasma proteomic technologies across multiple cohorts reveals generalizability of proteomic prediction

  • Anthony Barente
  • Zijie Zhao
  • Zhili Zheng
  • Chun Wu
  • Kritika Singh
  • Zhaoqing Wang
  • Aisling O’Hara Hall
  • Laurence Menard
  • Isaac Neuhaus
  • Mitja Kurki
  • Aarno Palotie
  • Mark Daly
7 more authors
  • Sarah Finer
  • David A. van Heel
  • Maik Pietzner
  • Claudia Langenberg
  • Joseph Maranville
  • Tai Wang
  • Benjamin B. Sun
Open at sourcePDFDOICite

Related

  1. Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology
    Ekaterina Redekop, Eric Zimmermann, Ava P. Amini +5
    Cell Reports MethodsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationPMC
  2. A systematic comparison of single-cell perturbation response prediction models
    Lanxiang Li, Yue You, Yunlin Fu +11
    Science AdvancesPeer-reviewedCC BY29 citedMolecular biology & gene regulationOpenAlex
  3. Machine learning prediction of rheumatoid arthritis using the gut microbiome shows limited cross-cohort generalizability
    Sabira Dabeer, Hatim Palitanawala
    Scientific ReportsPeer-reviewedCC BY 4.0Full textMolecular biology & gene regulationCrossref
  4. Proteomic Alterations in Hypomineralised Enamel: A Systematic Review.
    Gabriela Balixa, Bernhard Ganss, Luísa Bandeira Lopes
    Dentistry journalPeer-reviewedCC BYFull textMolecular biology & gene regulationEurope PMC
  5. Comparison of prohormone processing prediction using different artificial intelligence algorithms
    Bruce R. Southey, Jonathan V. Sweedler, Sandra L. Rodriguez-Zas
    In silico research in biomedicinePeer-reviewedCC BY-NC-NDFull text1 citedMolecular biology & gene regulationPMC

Cite

BibTeXRIS
BibTeX
@article{barente2026systematic,
  title = {{Systematic comparison of affinity plasma proteomic technologies across multiple cohorts reveals generalizability of proteomic prediction}},
  author = {Anthony Barente and Zijie Zhao and Zhili Zheng and Chun Wu and Kritika Singh and Zhaoqing Wang and Aisling O’Hara Hall and Laurence Menard and Isaac Neuhaus and Mitja Kurki and Aarno Palotie and Mark Daly and Sarah Finer and David A. van Heel and Maik Pietzner and Claudia Langenberg and Joseph Maranville and Tai Wang and Benjamin B. Sun},
  journal = {Nature Communications},
  year = {2026},
  month = oct,
  doi = {10.1038/s41467-026-78443-7},
  url = {https://www.nature.com/articles/s41467-026-78443-7},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Systematic comparison of affinity plasma proteomic technologies across multiple cohorts reveals generalizability of proteomic prediction
AU  - Anthony Barente
AU  - Zijie Zhao
AU  - Zhili Zheng
AU  - Chun Wu
AU  - Kritika Singh
AU  - Zhaoqing Wang
AU  - Aisling O’Hara Hall
AU  - Laurence Menard
AU  - Isaac Neuhaus
AU  - Mitja Kurki
AU  - Aarno Palotie
AU  - Mark Daly
AU  - Sarah Finer
AU  - David A. van Heel
AU  - Maik Pietzner
AU  - Claudia Langenberg
AU  - Joseph Maranville
AU  - Tai Wang
AU  - Benjamin B. Sun
PY  - 2026
DA  - 2026/10/08
JO  - Nature Communications
DO  - 10.1038/s41467-026-78443-7
UR  - https://www.nature.com/articles/s41467-026-78443-7
SN  - 2041-1723
ER  -